{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用feature_selector进一步筛选预处理（添加标签）过后的风机数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15_data.csv  21_data.csv  processed.7z\r\n"
     ]
    }
   ],
   "source": [
    "!ls ./train/processed/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>generator_speed</th>\n",
       "      <th>power</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_direction_mean</th>\n",
       "      <th>yaw_position</th>\n",
       "      <th>yaw_speed</th>\n",
       "      <th>pitch1_angle</th>\n",
       "      <th>pitch2_angle</th>\n",
       "      <th>...</th>\n",
       "      <th>environment_tmp</th>\n",
       "      <th>int_tmp</th>\n",
       "      <th>pitch1_ng5_tmp</th>\n",
       "      <th>pitch2_ng5_tmp</th>\n",
       "      <th>pitch3_ng5_tmp</th>\n",
       "      <th>pitch1_ng5_DC</th>\n",
       "      <th>pitch2_ng5_DC</th>\n",
       "      <th>pitch3_ng5_DC</th>\n",
       "      <th>group</th>\n",
       "      <th>frozen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01 20:20:16</td>\n",
       "      <td>1.859993</td>\n",
       "      <td>1.223595</td>\n",
       "      <td>2.515790</td>\n",
       "      <td>-2.072739</td>\n",
       "      <td>-2.073627</td>\n",
       "      <td>-0.655343</td>\n",
       "      <td>0.030804</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.403919</td>\n",
       "      <td>0.014918</td>\n",
       "      <td>1.307692</td>\n",
       "      <td>1.123077</td>\n",
       "      <td>0.783077</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-01 20:20:23</td>\n",
       "      <td>1.911625</td>\n",
       "      <td>1.293394</td>\n",
       "      <td>2.313551</td>\n",
       "      <td>-2.010591</td>\n",
       "      <td>-1.615140</td>\n",
       "      <td>-0.655343</td>\n",
       "      <td>0.030804</td>\n",
       "      <td>0.195556</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.421277</td>\n",
       "      <td>-0.002291</td>\n",
       "      <td>1.307692</td>\n",
       "      <td>1.123077</td>\n",
       "      <td>0.783077</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.88</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-01 20:20:30</td>\n",
       "      <td>1.635027</td>\n",
       "      <td>1.280099</td>\n",
       "      <td>2.507799</td>\n",
       "      <td>-2.053750</td>\n",
       "      <td>-0.282742</td>\n",
       "      <td>-0.649566</td>\n",
       "      <td>0.170338</td>\n",
       "      <td>0.964444</td>\n",
       "      <td>0.951111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.421277</td>\n",
       "      <td>-0.002291</td>\n",
       "      <td>1.307692</td>\n",
       "      <td>1.123077</td>\n",
       "      <td>0.783077</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-01 20:20:37</td>\n",
       "      <td>1.786234</td>\n",
       "      <td>1.280099</td>\n",
       "      <td>2.349593</td>\n",
       "      <td>-2.007138</td>\n",
       "      <td>-2.234477</td>\n",
       "      <td>-0.655343</td>\n",
       "      <td>-0.004080</td>\n",
       "      <td>0.168889</td>\n",
       "      <td>0.137778</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.403919</td>\n",
       "      <td>-0.002291</td>\n",
       "      <td>1.307692</td>\n",
       "      <td>1.123077</td>\n",
       "      <td>0.783077</td>\n",
       "      <td>2.80</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-01 20:20:47</td>\n",
       "      <td>1.786234</td>\n",
       "      <td>1.263480</td>\n",
       "      <td>2.321566</td>\n",
       "      <td>-2.264365</td>\n",
       "      <td>-1.428959</td>\n",
       "      <td>-0.637917</td>\n",
       "      <td>0.414524</td>\n",
       "      <td>0.182222</td>\n",
       "      <td>0.168889</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.403919</td>\n",
       "      <td>0.014918</td>\n",
       "      <td>1.307692</td>\n",
       "      <td>1.123077</td>\n",
       "      <td>0.783077</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  wind_speed  generator_speed     power  wind_direction  \\\n",
       "0  2015-11-01 20:20:16    1.859993         1.223595  2.515790       -2.072739   \n",
       "1  2015-11-01 20:20:23    1.911625         1.293394  2.313551       -2.010591   \n",
       "2  2015-11-01 20:20:30    1.635027         1.280099  2.507799       -2.053750   \n",
       "3  2015-11-01 20:20:37    1.786234         1.280099  2.349593       -2.007138   \n",
       "4  2015-11-01 20:20:47    1.786234         1.263480  2.321566       -2.264365   \n",
       "\n",
       "   wind_direction_mean  yaw_position  yaw_speed  pitch1_angle  pitch2_angle  \\\n",
       "0            -2.073627     -0.655343   0.030804      0.555556      0.506667   \n",
       "1            -1.615140     -0.655343   0.030804      0.195556      0.133333   \n",
       "2            -0.282742     -0.649566   0.170338      0.964444      0.951111   \n",
       "3            -2.234477     -0.655343  -0.004080      0.168889      0.137778   \n",
       "4            -1.428959     -0.637917   0.414524      0.182222      0.168889   \n",
       "\n",
       "    ...    environment_tmp   int_tmp  pitch1_ng5_tmp  pitch2_ng5_tmp  \\\n",
       "0   ...          -0.403919  0.014918        1.307692        1.123077   \n",
       "1   ...          -0.421277 -0.002291        1.307692        1.123077   \n",
       "2   ...          -0.421277 -0.002291        1.307692        1.123077   \n",
       "3   ...          -0.403919 -0.002291        1.307692        1.123077   \n",
       "4   ...          -0.403919  0.014918        1.307692        1.123077   \n",
       "\n",
       "   pitch3_ng5_tmp  pitch1_ng5_DC  pitch2_ng5_DC  pitch3_ng5_DC  group  frozen  \n",
       "0        0.783077           1.36           0.00           1.56      1       0  \n",
       "1        0.783077           0.44           2.88          -2.60      1       0  \n",
       "2        0.783077           1.76           0.60           2.56      1       0  \n",
       "3        0.783077           2.80          -0.48           0.12      1       0  \n",
       "4        0.783077          -0.88           1.72           0.92      1       0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train/processed/15_data.csv')\n",
    "train_labels = train['frozen']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns = ['frozen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "检测—Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 features with greater than 0.01 missing values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_missing(missing_threshold=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_features = fs.ops['missing']\n",
    "missing_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检测—Single Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 features with a single unique value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_single_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_unique = fs.ops['single_unique']\n",
    "single_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检测—Collinear (highly correlated) Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 features with a correlation magnitude greater than 0.97.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.975)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pitch2_angle',\n",
       " 'pitch3_angle',\n",
       " 'pitch2_speed',\n",
       " 'pitch3_speed',\n",
       " 'pitch2_moto_tmp']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlated_features = fs.ops['collinear']\n",
    "correlated_features[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear(plot_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drop_feature</th>\n",
       "      <th>corr_feature</th>\n",
       "      <th>corr_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pitch2_angle</td>\n",
       "      <td>pitch1_angle</td>\n",
       "      <td>0.999563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pitch3_angle</td>\n",
       "      <td>pitch1_angle</td>\n",
       "      <td>0.999356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pitch3_angle</td>\n",
       "      <td>pitch2_angle</td>\n",
       "      <td>0.999525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pitch2_speed</td>\n",
       "      <td>pitch1_speed</td>\n",
       "      <td>0.992649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pitch3_speed</td>\n",
       "      <td>pitch1_speed</td>\n",
       "      <td>0.990925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drop_feature  corr_feature  corr_value\n",
       "0  pitch2_angle  pitch1_angle    0.999563\n",
       "1  pitch3_angle  pitch1_angle    0.999356\n",
       "2  pitch3_angle  pitch2_angle    0.999525\n",
       "3  pitch2_speed  pitch1_speed    0.992649\n",
       "4  pitch3_speed  pitch1_speed    0.990925"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.record_collinear.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检测—Zero Importance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-d01bb5ef4ad5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m fs.identify_zero_importance(task = 'classification', eval_metric = 'auc', \n\u001b[0;32m----> 2\u001b[0;31m                             n_iterations = 1, early_stopping = True)\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/feature_selector-N_A-py3.6.egg/feature_selector/feature_selector.py\u001b[0m in \u001b[0;36midentify_zero_importance\u001b[0;34m(self, task, eval_metric, n_iterations, early_stopping)\u001b[0m\n\u001b[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0;31m# One hot encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m         \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_hot_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_features\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas-0.23.4-py3.6-linux-x86_64.egg/pandas/core/reshape/reshape.py\u001b[0m in \u001b[0;36mget_dummies\u001b[0;34m(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first, dtype)\u001b[0m\n\u001b[1;32m    890\u001b[0m             dummy = _get_dummies_1d(col[1], prefix=pre, prefix_sep=sep,\n\u001b[1;32m    891\u001b[0m                                     \u001b[0mdummy_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdummy_na\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m                                     drop_first=drop_first, dtype=dtype)\n\u001b[0m\u001b[1;32m    893\u001b[0m             \u001b[0mwith_dummies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwith_dummies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas-0.23.4-py3.6-linux-x86_64.egg/pandas/core/reshape/reshape.py\u001b[0m in \u001b[0;36m_get_dummies_1d\u001b[0;34m(data, prefix, prefix_sep, dummy_na, sparse, drop_first, dtype)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 980\u001b[0;31m         \u001b[0mdummy_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber_of_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdummy_na\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy-1.15.2-py3.6-linux-x86_64.egg/numpy/lib/twodim_base.py\u001b[0m in \u001b[0;36meye\u001b[0;34m(N, M, k, dtype, order)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mM\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m     \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(task = 'classification', eval_metric = 'auc', \n",
    "                            n_iterations = 10, early_stopping = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 28 original features\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'NoneType' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-f78c39216741>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mbase_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'There are %d original features'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'There are %d one-hot features'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_hot_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
     ]
    }
   ],
   "source": [
    "one_hot_features = fs.one_hot_features\n",
    "base_features = fs.base_features\n",
    "print('There are %d original features' % len(base_features))\n",
    "print('There are %d one-hot features' % len(one_hot_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检测—Low Importance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Feature importances have not yet been determined. \n                                         Call the `identify_zero_importance` method first.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-eea19cd3045b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentify_low_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcumulative_importance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.99\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/feature_selector-N_A-py3.6.egg/feature_selector/feature_selector.py\u001b[0m in \u001b[0;36midentify_low_importance\u001b[0;34m(self, cumulative_importance)\u001b[0m\n\u001b[1;32m    361\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m             raise NotImplementedError(\"\"\"Feature importances have not yet been determined. \n\u001b[0;32m--> 363\u001b[0;31m                                          Call the `identify_zero_importance` method first.\"\"\")\n\u001b[0m\u001b[1;32m    364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m         \u001b[0;31m# Make sure most important features are on top\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: Feature importances have not yet been determined. \n                                         Call the `identify_zero_importance` method first."
     ]
    }
   ],
   "source": [
    "fs.identify_low_importance(cumulative_importance = 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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